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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning

Harnessing the power of big data has become increasingly critical for businesses looking to gain a competitive edge. However, managing the complex infrastructure required for big data workloads has traditionally been a significant challenge, often requiring specialized expertise. elasticmapreduce", "arn:aws:s3:::*.elasticmapreduce/*"

Big data 125
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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

On the JSON tab, modify the policy as follows: { "Version": "2012-10-17", "Statement": [ { "Sid": "eniperms", "Effect": "Allow", "Action": [ "ec2:CreateNetworkInterface", "ec2:DescribeNetworkInterfaces", "ec2:DeleteNetworkInterface", "ec2:*VpcEndpoint*" ], "Resource": "*" } ] } Choose Next. You’re redirected to the IAM console. With an M.Sc.

APIs 143
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Set up cross-account Amazon S3 access for Amazon SageMaker notebooks in VPC-only mode using Amazon S3 Access Points

AWS Machine Learning

Kesaraju Sai Sandeep is a Cloud Engineer specializing in Big Data Services at AWS. Don’t change or edit any Block Public Access settings for this access point (all public access should be blocked). You can define the actions as per your requirements or use case.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

Scripts 130
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Large-scale feature engineering with sensitive data protection using AWS Glue interactive sessions and Amazon SageMaker Studio

AWS Machine Learning

Create IAM users called data-engineer and data-scientist under the IAM group data-platform-group. Modify your role’s trust relationship: { "Version":"2012-10-17", "Statement":[ { "Effect":"Allow", "Principal":{ "Service":[ "glue.amazonaws.com", "sagemaker.amazonaws.com" ] }, "Action":"sts:AssumeRole" } ] }.

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Define customized permissions in minutes with Amazon SageMaker Role Manager

AWS Machine Learning

To allow your data scientists to assume their given persona via the console, they require a console role to get to the Studio environment. He is passionate about building secure and scalable AI/ML and big data solutions to help enterprise customers with their cloud adoption and optimization journey to improve their business outcomes.

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Securing MLflow in AWS: Fine-grained access control with AWS native services

AWS Machine Learning

He regularly speaks at AI and machine learning conferences across the world including O’Reilly AI, Open Data Science Conference, and Big Data Spain. mlflow/runs/search/", "arn:aws:execute-api: : : / /POST/api/2.0/mlflow/experiments/search", mlflow/runs/search/", "arn:aws:execute-api: : : / /POST/api/2.0/mlflow/experiments/search",

APIs 92